Advanced Econometrics (Econometrics II)

Advanced Econometrics

Advanced Econometrics builds on the basic econometrics course in three directions. First, the course discusses a range of practical methods used in economics and business. Second, it addresses both causal inference and forecasting. Third, it offers a deeper discussion of the underlying theory.

Course Information

Instructor: Vladislav Morozov
Email: morozov [at] uni-bonn.de
Office Location: Adenauerallee 24-42, IFS, Statistics Section
Office Hours: Virtual, by appointment
Course Website: eCampus and this website
Lectures: 8:30-10:00; Wednesdays (Room 0.042), Fridays (Lecture Hall N)
Schedule Changes and Holidays: See eCampus and BASIS
DOI: DOI

Level: Undergraduate
Prerequisites: basic courses in statistics and econometrics

In Slide Form

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Course Content

Content

The course is structured as follows (subject to change):

  1. A deeper look at linear regression:
    • A vector-matrix form approach to linear regression.
    • Basics of identification analysis.
    • Asymptotic theory for the OLS estimator.
  2. Asymptotic inference:
    • Refresher: key definitions and intuition of hypothesis testing.
    • Tests for linear hypotheses: \(t\)- and Wald tests.
    • The delta method and nonlinear Wald tests.
  3. Panel data in causal settings:
    • Event studies.
    • Differences-in-differences.
    • Two-way fixed effect approaches with multivalued treatment.
    • Mean group estimation.
  4. Introduction to forecasting:
    • Causal inference vs. forecasting I.
    • Notions of forecast optimality.
    • Forecasting in cross-sections.
  5. Parametric nonlinear models:
    • Beyond linearity: nonlinear regression and nonlinear least squares.
    • Discrete outcomes in causal settings.
    • Elements of asymptotic theory for nonlinear models.
    • Classification as forecasting with discrete outcomes.

If time allows, we will further discuss:

  1. Generalized method of moments.
    • Linear generalized method of moments (GMM).
    • IV estimation of dynamic panel data models.
    • Fundamentals of nonlinear GMM.
  2. Time series:
    • Time series as probabilistic objects and their properties.
    • Univariate models: ARIMA(X).
    • Multivariate time series: VARIMA(X).
    • Elements of causal inference with time series.
    • Forecasting with time series vs. forecasting with panel data

Even further topics such as quantile regression, experimentation under interference, and high-dimensional data may be introduced as time allows.

Course Materials

Textbooks: the course draws on several textbooks:

  • Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer International Publishing.

  • Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.

  • Huntington-Klein, N. (2025). The Effect: An Introduction to Research Design and Causality. Chapman and Hall/CRC.

  • James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. E. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer.

  • Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach (Seventh edition). Cengage.

All books minus Wooldridge are available online (openly or through our university network). The Wooldridge book is available in our library. For specific chapters, please refer to each specific set of slides.

Course Policies

Grading and Evaluation

The final grade for this course is based on a 90 minute closed-book written exam. The date of the exam will be announced separately by the Examination Office.

Policies and Additional Information

Attendance and Participation:
Regular attendance and active participation are strongly encouraged.

Academic Integrity:
Students must adhere to the university’s policies on academic integrity and plagiarism. Any violations will be subject to disciplinary action.

Accommodations:
If you require any accommodations due to a disability or other circumstances, please contact the relevant office as soon as possible.